Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

121
The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
121
Rheumatic Heart Disease I: Introduction01:23

Rheumatic Heart Disease I: Introduction

97
Rheumatic heart disease or RHD is a chronic condition that results from rheumatic fever, causing permanent damage to the heart valves.Etiology and Risk FactorsIt primarily arises from rheumatic fever, an inflammatory disease that can develop after untreated or inadequately treated group A streptococcal (GAS) pharyngitis. Streptococcus spreads through direct contact with oral or respiratory secretions. While the bacteria are the causative agents, factors like malnutrition, overcrowding, poor...
97
Rheumatic Heart Disease III: Medical Management01:21

Rheumatic Heart Disease III: Medical Management

58
Rheumatic heart disease (RHD) management can be divided into two main strategies: prevention and long-term management.Primary PreventionPrimary prevention focuses on timely diagnosis and management of group A streptococcal pharyngitis to prevent acute rheumatic fever. The most widely used antibiotic for treating this condition is intramuscular benzathine penicillin G.Acute Rheumatic Fever TreatmentThe primary treatment goal for a patient diagnosed with acute rheumatic fever is to suppress the...
58
Rheumatic Heart Disease IV: Nursing Management01:20

Rheumatic Heart Disease IV: Nursing Management

66
AssessmentA comprehensive assessment is essential in managing a patient with rheumatic heart disease (RHD). Begin with obtaining a detailed medical history, including recent streptococcal infections, a history of rheumatic fever, or previously diagnosed rheumatic heart disease. Assess the patient for symptoms such as fever, chest pain, widespread joint pain (arthralgia), tachycardia, pericardial friction rub, muffled heart sounds, heart murmurs, peripheral edema, subcutaneous nodules, and...
66
Assessment of apical radial pulse01:25

Assessment of apical radial pulse

926
Apical-Radial (A-R) Pulse Assessment
The A-R pulse assessment involves simultaneous evaluation of the apical and radial pulses. When the apical and radial pulse rates vary, this assessment helps identify a pulse deficit.
Pre-Procedural Preparation
926
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

1.8K
To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
1.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.

JMIR medical informatics·2026
Same author

Prevalence of Early Rheumatic Heart Disease Among Asymptomatic Students in Underserved Communities in Ethiopia: Cross-Sectional Observational Study.

JMIR public health and surveillance·2026
Same author

Genomic history of early dogs in Europe.

Nature·2026
Same author

An Affordable Smartphone-based Fundus Imaging Device for Retinal Examination.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Machine learning for classification of advanced rheumatic heart disease using electrocardiogram in cardiology ward.

BMC cardiovascular disorders·2025
Same author

Pattern of extra-diaphragmatic respiratory muscle activity during exercise in patients with unilateral diaphragm dysfunction.

Physiological reports·2025

Related Experiment Video

Updated: Oct 17, 2025

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

3.9K

Rheumatic Heart Disease Screening Based on Phonocardiogram.

Melkamu Hunegnaw Asmare1,2, Benjamin Filtjens1,3, Frehiwot Woldehanna2

  • 1eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces an automated machine learning tool for early Rheumatic Heart Disease (RHD) detection, offering a cost-effective solution for mass screening in developing countries. The system demonstrates high accuracy, aiding non-medical personnel in identifying RHD through heart sound analysis.

Keywords:
acoustic featuresnested cross-validationphonocardiogramrheumatic heart diseasescreeningsupport vector machines

More Related Videos

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

583
Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients
08:45

Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients

Published on: April 18, 2025

527

Related Experiment Videos

Last Updated: Oct 17, 2025

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

3.9K
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

583
Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients
08:45

Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients

Published on: April 18, 2025

527

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Rheumatic Heart Disease (RHD) is a significant cause of cardiovascular morbidity in developing nations, primarily affecting children.
  • Current diagnostic methods like manual auscultation lack sensitivity and specificity, while echocardiography is resource-intensive.
  • High RHD prevalence necessitates accessible and accurate early detection strategies for effective intervention.

Purpose of the Study:

  • To develop and validate an automated screening tool for Rheumatic Heart Disease (RHD) utilizing machine learning.
  • To enable early detection of RHD by non-medically trained individuals in community settings.
  • To address the limitations of current RHD diagnostic methods in resource-constrained environments.

Main Methods:

  • Collected heart sound data from 124 individuals with RHD and 46 healthy controls, supplemented by 81 healthy control records from an open dataset.
  • Extracted 31 distinct features from heart sound data to characterize RHD.
  • Employed a Support Vector Machine (SVM) classifier, evaluated using nested cross-validation for robust performance assessment.

Main Results:

  • Achieved an f1-score of 96.0%, recall of 95.8%, precision of 96.2%, and specificity of 96.0% in standard cross-validation.
  • In imbalanced validation simulating low prevalence (5%), the system yielded an f1-score of 72.2%, recall of 92.3%, precision of 59.2%, and specificity of 94.8%.
  • Demonstrated high recall, crucial for screening in low-prevalence populations, indicating strong potential for early detection.

Conclusions:

  • The proposed machine learning-based RHD screening tool is accurate, cost-effective, and user-friendly.
  • The system's ease of deployment and high detection rates support its application in mass screening programs for RHD in developing countries.
  • This automated approach holds significant promise for improving early diagnosis and management of RHD, thereby reducing cardiovascular complications.